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Data:
586111 604378 600991 544686 537034 551531 563250 574761 580112 575093 557560 564478 580523 596594 586570 536214 523597 536535 536322 532638 528222 516141 501866 506174 517945 533590 528379 477580 469357 490243 492622 507561 516922 514258 509846 527070 541657 564591 555362 498662 511038 525919 531673 548854 560576 557274 565742 587625 619916 625809 619567 572942 572775 574205 579799 590072 593408 597141 595404 612117 628232 628884 620735 569028 567456 573100 584428 589379 590865 595454 594167 611324 612613 610763 593530 542722 536662 543599 555332 560854 562325 554788 547344 565464 577992 579714 569323 506971 500857 509127 509933 517009 519164 512238 509239 518585 522975 525192 516847 455626 454724 461251 470439 474605 476049 471067 470984 502831 512927 509673 484015 431328 436087 442867 447988 460070 467037 460170 464196 485025 501492 520564 488180 439148 441977 456608 461935 480961 492865
# simulations
blockwidth of bootstrap
Significant digits
Quantiles
P1 P5 Q1 Q3 P95 P99
P0.5 P2.5 Q1 Q3 P97.5 P99.5
P10 P20 Q1 Q3 P80 P90
bandwidth
Chart options
R Code
par1 <- as.numeric(par1) par2 <- as.numeric(par2) if (par1 < 10) par1 = 10 if (par1 > 5000) par1 = 5000 if (par2 < 3) par2 = 3 if (par2 > length(x)) par2 = length(x) library(lattice) library(boot) boot.stat <- function(s) { s.mean <- mean(s) s.median <- median(s) c(s.mean, s.median) } (r <- tsboot(x, boot.stat, R=par1, l=12, sim='fixed')) z <- data.frame(cbind(r$t[,1],r$t[,2])) colnames(z) <- list('mean','median') bitmap(file='plot7.png') b <- boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency') grid() dev.off() b load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Estimation Results of Blocked Bootstrap',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'statistic',header=TRUE) a<-table.element(a,'Q1',header=TRUE) a<-table.element(a,'Estimate',header=TRUE) a<-table.element(a,'Q3',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'IQR',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) q1 <- quantile(r$t[,1],0.25)[[1]] q3 <- quantile(r$t[,1],0.75)[[1]] a<-table.element(a,q1) a<-table.element(a,r$t0[1]) a<-table.element(a,q3) a<-table.element(a,sqrt(var(r$t[,1]))) a<-table.element(a,q3-q1) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'median',header=TRUE) q1 <- quantile(r$t[,2],0.25)[[1]] q3 <- quantile(r$t[,2],0.75)[[1]] a<-table.element(a,q1) a<-table.element(a,r$t0[2]) a<-table.element(a,q3) a<-table.element(a,sqrt(var(r$t[,2]))) a<-table.element(a,q3-q1) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'95% Confidence Intervals',3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) a<-table.element(a,'Mean',1,TRUE) a<-table.element(a,'Median',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Lower Bound',1,TRUE) a<-table.element(a,b$conf[1,1]) a<-table.element(a,b$conf[1,2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Upper Bound',1,TRUE) a<-table.element(a,b$conf[2,1]) a<-table.element(a,b$conf[2,2]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab')
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